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Proceedings of the International Conference on Material Theory and Nonlinear Dynamics. MatDyn. Hanoi, Vietnam, Sept. 24-26, 2007, 8 p. In this paper, a method is introduced to determine the limit load of general shells using the finite element method. The method is based on an upper bound limit and shakedown analysis with elastic-perfectly plastic material model. A non-linear constrained optimisation problem is solved by using Newton’s method in conjunction with a penalty method and the Lagrangean dual method. Numerical investigation of a pipe bend subjected to bending moments proves the effectiveness of the algorithm.
[Paper of the X International Symposium on Lightning Protection 9th - 13th November, 2009 - Curitiba, Brazil. 6 pages] The international standard IEC 62305-3, published in 2006, requires as an integral part of the lightning protection system (LPS) the consideration of a separation distance between the conductors of the LPS and metal and electrical installations inside the structure to be protected. IEC 62305-3 gives two different methods for this calculation: a standard, simplified approach and a more detailed approach, which differ especially regarding the treatment of the current sharing effect on the LPS conductors. Hence, different results for the separation distance are possible, leading to some discrepancies in the use of the standard. The standard approach defined in the main part (Clause 6.3) and in Annex C of the standard in some cases may lead to a severe oversizing of the required separation distance. The detailed approach described in Annex E naturally gives more correct results. However, a calculation of the current sharing amongst all parts of the air-termination and downconductor network is necessary, in many cases requiring the use of network analysis programs. In this paper simplified methods for the assessment of the current sharing are presented, which are easy to use as well as sufficiently adequate.
The integration of frequently changing, volatile product data from different manufacturers into a single catalog is a significant challenge for small and medium-sized e-commerce companies. They rely on timely integrating product data to present them aggregated in an online shop without knowing format specifications, concept understanding of manufacturers, and data quality. Furthermore, format, concepts, and data quality may change at any time. Consequently, integrating product catalogs into a single standardized catalog is often a laborious manual task. Current strategies to streamline or automate catalog integration use techniques based on machine learning, word vectorization, or semantic similarity. However, most approaches struggle with low-quality or real-world data. We propose Attribute Label Ranking (ALR) as a recommendation engine to simplify the integration process of previously unknown, proprietary tabular format into a standardized catalog for practitioners. We evaluate ALR by focusing on the impact of different neural network architectures, language features, and semantic similarity. Additionally, we consider metrics for industrial application and present the impact of ALR in production and its limitations.